Embodiments of the present invention relate to video test and measurement and more particularly to an improvement to core video picture quality (PQ) measurements, such as subjective video quality predictions for causal viewers.
Video compression methods such as MPEG-2 and H.264 process video use lossy compression methods and introduce errors, ideally unseen by the human eye. Any visible error caused by loss in the compression method manifests itself as an impairment artifact which generally can be distracting to the viewer. In addition, other aspects of the video image draw the viewers attention. Algorithms used to predict the probability density of focus of attention over space and time due to these other non-distraction attention attracters have been developed and are often referred to as “attention models.” The term “distraction” here refers to video impairments and more general deviations from a video reference rather than intended content in the video, which may have its own types of “distractions.”
Attention models developed thus far, when used in conjunction with other video measurements such as perceptual difference prediction models, etc., when measuring video with distractions, generally don't improve predictions of subjective ratings any more than if the attention model were removed. However, much research has shown that when distractions are not present, prediction of the probability of focus of attention can be quite good. Also, it is known that, depending on how much the video display occupies the field of view, human peripheral vision is substantially less sensitive to spatial distortions than “foveal” or center of vision. Thus, a key missing piece for the prediction of how visible video degradation will be depends on where people look, including when they look at aforementioned distractions.